A CT-based deep learning model: visceral pleural invasion and survival prediction in clinical stage IA lung adenocarcinoma

被引:2
|
作者
Lin, Xiaofeng [1 ]
Liu, Kunfeng [1 ]
Li, Kunwei [1 ,2 ]
Chen, Xiaojuan [3 ]
Chen, Biyun [1 ]
Li, Sheng [1 ]
Chen, Huai [4 ]
Li, Li [1 ]
机构
[1] Sun Yat Sen Univ, Guangdong Prov Clin Res Ctr Canc, Dept Med Imaging, State Key Lab Oncol South China,Canc Ctr, Guangzhou 510060, Peoples R China
[2] Sun Yat Sen Univ, Affiliated Hosp 5, Dept Radiol, Zhuhai 519000, Peoples R China
[3] Guangzhou Med Univ, Affiliated Hosp 1, Dept Radiol, Guangzhou 510260, Peoples R China
[4] Guangzhou Med Univ, Dept Neurol, Affiliated Hosp 2, Guangzhou 510260, Peoples R China
关键词
LESS-THAN-OR-EQUAL-TO-3; CM; ADJUVANT CHEMOTHERAPY; PROGNOSTIC-FACTOR; 8TH EDITION; CANCER; IMPACT; CLASSIFICATION;
D O I
10.1016/j.isci.2023.108712
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Pathologic visceral pleural invasion (VPI) in patients with early-stage lung cancer can result in the upstaging of T1 to T2, in addition to having implications for surgical resection and prognostic outcomes. This study was designed with the goal of establishing and validating a CT-based deep learning (DL) model capable of predicting VPI status and stratifying patients based on their prognostic outcomes. In total, 2077 patients from three centers with pathologically confirmed clinical stage IA lung adenocarcinoma were enrolled. DL signatures were extracted with a 3D residual neural network. DL model was able to effectively predict VPI status. VPI predicted by the DL models, as well as pathologic VPI, was associated with shorter disease-free survival. The established deep learning signature provides a tool capable of aiding the accurate prediction of VPI in patients with clinical stage IA lung adenocarcinoma, thus enabling prognostic stratification.
引用
收藏
页数:19
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